Hardware algorithm design and validation for nuclear radiation imaging based on Monte Carlo simulation

The nuclear radiation imaging technology, aimed at illustrating the position and distribution of radioactive sources, has undergone extensive research. By relying on a simulated radiation imaging system for data acquisition, we can significantly expedite the development cycle of these imaging instruments. Establishing simulated experimental scenarios and radiation imaging systems is of paramount significance in obtaining output signals for algorithmic testing and validation. This study is divided into two parts: simulation and hardware algorithm. In the simulation part, precise simulation of scintillation light transport in a crystal was achieved using the GEANT4 Monte Carlo simulation toolkit. A LaBr3(Ce) detector system was simulated by digitizing photon interactions. In the hardware algorithm part, a positioning algorithm based on a fully connected neural network was implemented and optimized using a heterogeneous distributed storage approach. The system validated and assessed the FPGA-based neural network gamma camera positioning algorithm, demonstrating significant consistency with computer-generated images in capturing the shape and dispersion of radioactive sources (planar, multi-point, and ring-shaped). •Accurate simulation of scintillation light transport in crystals was simulated in GEANT4.•An experimental scenarios and radiation imaging systems has been Established in GEANT4.•A fully connected neural network for gamma ray positioning based on FPGA has been developed and validated.